Updating fault transmissibilities in simulations by successively adding data to an automated seismic history matching processes: a case study

Reservoir management may be improved if the present state of the field is known and if changes may be predicted. The former requires information about current fluid sweep and Pressure changes while the latter requires accurate reservoir description and a predictive tool such as a simulation model. With this information, important decisions can then be made including facility maintenance and well optimisation. In some reservoirs, faults control flow behaviour and, their flow properties have traditionally been treated as a history matching parameter by applying a transmissibility multiplier. We present an automated history matching method which updates fault transmissibilities by matching 4D seismic predictions from the simulations to observed data. We investigate the impact of successively updating faults by adding new data to our observed dataset and compare this to a single history match where all data is used. We demonstrate the method by applying it to the UKCS Schiehallion reservoir using six years of production data and six seismic surveys. We update an upscaled version of the operators model for increased speed and account for upscaling errors in the misfit function We consider a number of parameters to be uncertain including fault transmissibilities. Our results show a good match to the observed seismic and dynamic well data with significant improvement to the base case. The best result occurs when early data is used in short simulations first as we learn about optimum parameter values. Later data may be added for fine tuning or to explore new parameters. Successive updating of the fault transmissibility multLpliers increases the accuracy and reliability of the simulation predictions and has great impact on the reservoir management decisions. Bypassed oil regions are identified with much greater confidence enabling better infill well placement with less risk.